Accumulating and labeling a sufficient number of data points for use in unsupervised or semi-supervised machine learning algorithms to detect anomalies may be challenging. Conventional approaches to detecting anomalies using such unsupervised or semi-supervised machine learning algorithms may result in a substantial number of false positives.